{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Scientific Programming in Python (SPiP) - Data Analysis with Pandas\n",
    "\n",
    "\n",
    "by [Michael Granitzer (michael.granitzer@uni-passau.de)](http://www.mendeley.com/profiles/michael-granitzer/) \n",
    "\n",
    "based on the following sources\n",
    "\n",
    "* [Pandas Documentation](http://pandas.pydata.org/pandas-docs/stable/pandas.pdf)\n",
    "* [Marcel Caraciolo](https://github.com/marcelcaraciolo), \n",
    "  [Big Data Tutorial](https://github.com/marcelcaraciolo/big-data-tutorial)\n",
    "\n",
    "\n",
    "__License__\n",
    "\n",
    "This work is licensed under a [Creative Commons Attribution 3.0 Unported License](http://creativecommons.org/licenses/by/3.0/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#Content and Goals\n",
    "\n",
    "##Content\n",
    "\n",
    "* What is Pandas?\n",
    "* Basic Data Structures: Series and Data Frame\n",
    "* Basic Functions\n",
    "* Descriptive statistic\n",
    "* IO Tools\n",
    "* Visualisation with Pandas\n",
    "\n",
    "##Goals \n",
    "* Multivariate Data processing\n",
    "* Loading, filtering, analysing data sets\n",
    "* Basic visualisation/plotting functions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 1. What is Pandas\n",
    "\n",
    "*pandas is a Python package providing fast, ﬂexible, and expressive data structures designed to make working with\n",
    "“relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing\n",
    "practical, real world data analysis in Python* (http://pandas.pydata.org/) \n",
    "\n",
    "Pandas builds on top of Numpy to ease managing heterogeneous data sets.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 1.1 Data Handled by Pandas\n",
    "\n",
    "Pandas is well suited for many different kinds of data:\n",
    "\n",
    "* Tabular data with heterogeneously-typed columns (comparable to EXCEL, R or relational Databases)\n",
    "* Time series data\n",
    "*  Matrix data(homogeneously typed or heterogeneous) with row and column labels\n",
    "* Any other form of observational / statistical data sets. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 1.2 Feature Overview\n",
    "\n",
    "* Easy handling of missing data (represented as NaN)\n",
    "* Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects\n",
    "* Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations\n",
    "* Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both ag- gregating and transforming data\n",
    "* Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects\n",
    "* Intelligent label-based slicing, fancy indexing, and subsetting of large data sets\n",
    "* Intuitive merging and joining data sets\n",
    "* Flexible reshaping and pivoting of data sets\n",
    "* Hierarchical labeling of axes (possible to have multiple labels per tick)\n",
    "* Robust IO tools for loading and storing data \n",
    "* Time series-specific functionality\n",
    "* Fast realization in Cython\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 2. Pandas Data Structures\n",
    "\n",
    "Pandas is build around two data structures\n",
    "\n",
    "* `Series` represent 1 dimensional datasets as subclass of Numpy's ndarray\n",
    "* `DataFrame` represent 2 dimensional data sets as list of `Series`\n",
    "* `Panel` represents higher dimensional data as dictionaries of `DataFrame`'s. We do not discuss `Panel`'s here.\n",
    "\n",
    "For all data structures, labels/indices can be defined per row and column. \n",
    "\n",
    "Data alignment is intrinsict, i.e. the link between labels and data will not be broken.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 2.1. Series: labelled arrays\n",
    "\n",
    "Series is a one-dimensional labeled array capable of holding any data type (integers, strings, ﬂoating point numbers,Python objects, etc.). The axis labels are collectively referred to as the index. The basic method to create a Series is\n",
    "to call:\n",
    "\n",
    "    Series(data, index=index)\n",
    "    \n",
    "`data` may be a dict, a `numpy.ndarray` or a sclar value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**Creating a series**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     2.0000\n",
      "1     1.0000\n",
      "2     5.0000\n",
      "3     0.9700\n",
      "4     3.0000\n",
      "5    10.0000\n",
      "6     0.0599\n",
      "7     8.0000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # we need numpy\n",
    "import pandas as pd #import pandas\n",
    "\n",
    "\n",
    "values = np.array([2.0, 1.0, 5.0, 0.97, 3.0, 10.0, 0.0599, 8.0]) #create numpy array\n",
    "ser = pd.Series(values) # now create series\n",
    "print (ser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A     2.0000\n",
      "B     1.0000\n",
      "C     5.0000\n",
      "D     0.9700\n",
      "E     3.0000\n",
      "F    10.0000\n",
      "G     0.0599\n",
      "H     8.0000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#now lets put some index on the data\n",
    "#indices will be great later on when accessing datas\n",
    "#indices represent unique identifiers for rows\n",
    "values = np.array([2.0, 1.0, 5.0, 0.97, 3.0, 10.0, 0.0599, 8.0])\n",
    "labels = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']\n",
    "ser = pd.Series(data=values, index=labels)#values and labels must have the same dimension\n",
    "print (ser)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age                                                1\n",
      "gender                                             F\n",
      "genres                                         Drama\n",
      "movie_id                                        1193\n",
      "occupation                                        10\n",
      "rating                                             5\n",
      "timestamp                                  978300760\n",
      "title         One Flew Over the Cuckoo's Nest (1975)\n",
      "user_id                                            1\n",
      "zip                                            48067\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#craeting a heterogeneous series\n",
    "movie_rating = {\n",
    "    'age': 1,\n",
    "    'gender': 'F',\n",
    "    'genres': 'Drama',\n",
    "    'movie_id': 1193,\n",
    "    'occupation': 10,\n",
    "    'rating': 5,\n",
    "    'timestamp': 978300760,\n",
    "    'title': \"One Flew Over the Cuckoo's Nest (1975)\",\n",
    "    'user_id': 1,\n",
    "    'zip': '48067'\n",
    "    }\n",
    "ser = pd.Series(movie_rating)\n",
    "print (ser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'gender', 'genres', 'movie_id', 'occupation', 'rating',\n",
       "       'timestamp', 'title', 'user_id', 'zip'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show the index\n",
    "ser.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 'F', 'Drama', 1193, 10, 5, 978300760,\n",
       "       \"One Flew Over the Cuckoo's Nest (1975)\", 1, '48067'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show the value\n",
    "ser.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Series Indexing**\n",
    "\n",
    "Accessing elements in a series can be either done via the number or the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#use the row number\n",
    "ser[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#use the index like in a dict object\n",
    "ser['gender']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.get_value('gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Operations between Series with different index objects**\n",
    "\n",
    "Index objects are unique and used in operations between series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A    6.0\n",
      "B    NaN\n",
      "C    9.0\n",
      "G    NaN\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "ser_1 = pd.Series(data=[1,3,4], index=['A', 'B', 'C']) #series 1\n",
    "ser_2 = pd.Series(data=[5,5,5], index=['A', 'G', 'C']) #series 2 but with different indices\n",
    "print (ser_1 + ser_2) #note that if an index not exists, its value is undefined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**A `Series` is like a `Numpy.ndarray`**\n",
    "\n",
    "It can be used in Numpy functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    1.695949\n",
      "b   -0.137781\n",
      "c    1.753897\n",
      "d   -0.919139\n",
      "e   -0.799498\n",
      "dtype: float64\n",
      "epx(s)\n",
      " a    5.451815\n",
      "b    0.871289\n",
      "c    5.777074\n",
      "dtype: float64\n",
      "a    1.695949\n",
      "b   -0.137781\n",
      "c    1.753897\n",
      "d   -0.919139\n",
      "e   -0.799498\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "s=pd.Series(np.random.randn(5),index=[\"a\", \"b\", \"c\", \"d\", \"e\"])\n",
    "print (s)\n",
    "print (\"epx(s)\\n\",np.exp(s[:3]))\n",
    "print (s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Naming a series**\n",
    "\n",
    "A series can have a name, i.e. a series label (or if viewed column wise the name of the column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    1.695949\n",
      "b   -0.137781\n",
      "c    1.753897\n",
      "d   -0.919139\n",
      "e   -0.799498\n",
      "Name: Name of my Series, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "s.name=\"Name of my Series\"\n",
    "print (s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 2.2. DataFrame: a Series of Series\n",
    "\n",
    "The pandas DataFrame is a 2 dimensional labeled data structure with columns of potentially different types. Similar to \n",
    "* a spreadsheet \n",
    "* relational database table \n",
    "* a dictionary of series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Creating DataFrame's**\n",
    "\n",
    "DataFrame's can be created from\n",
    "* dict of Series\n",
    "* dict of ndarrays\n",
    "* structured or record arrays\n",
    "* from a list of dicts\n",
    "* From a dict of tuples\n",
    "* From a Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   one  two\n",
      "a  1.0  1.0\n",
      "b  2.0  2.0\n",
      "c  3.0  3.0\n",
      "d  NaN  4.0\n",
      "   one  two\n",
      "d  NaN  4.0\n",
      "b  2.0  2.0\n",
      "a  1.0  1.0\n",
      "Index: Index(['a', 'b', 'c', 'd'], dtype='object')\n",
      "Columns: Index(['one', 'two'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#dict of series\n",
    "d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),\n",
    "    'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}\n",
    "df = pd.DataFrame(d)\n",
    "print (df)\n",
    "print (pd.DataFrame(d,index=['d','b','a'])) # create data frame with index subset\n",
    "print\n",
    "print (\"Index:\", df.index)\n",
    "print (\"Columns:\", df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   one  two\n",
      "0  1.0  1.0\n",
      "1  2.0  2.0\n",
      "2  3.0  3.0\n",
      "3  4.0  4.0\n"
     ]
    }
   ],
   "source": [
    "#dict of lists\n",
    "d = {'one' : [1., 2., 3., 4.],\n",
    "    'two' :  [1., 2., 3., 4.]}\n",
    "print (pd.DataFrame(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a   b     c\n",
      "0  1   2   NaN\n",
      "1  5  10  20.0\n"
     ]
    }
   ],
   "source": [
    "#form a list of dicts\n",
    "data2 = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]\n",
    "print (pd.DataFrame(data2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       a              b      \n",
      "       a    b    c    a     b\n",
      "A B  4.0  1.0  5.0  8.0  10.0\n",
      "  C  3.0  2.0  6.0  7.0   NaN\n",
      "  D  NaN  NaN  NaN  NaN   9.0\n"
     ]
    }
   ],
   "source": [
    "# a dict of tuples\n",
    "print (pd.DataFrame({('a', 'b'): {('A', 'B'): 1, ('A', 'C'): 2},\n",
    " ('a', 'a'): {('A', 'C'): 3, ('A', 'B'): 4},\n",
    " ('a', 'c'): {('A', 'B'): 5, ('A', 'C'): 6},\n",
    " ('b', 'a'): {('A', 'C'): 7, ('A', 'B'): 8},\n",
    " ('b', 'b'): {('A', 'D'): 9, ('A', 'B'): 10}}) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Column selection, addition, deletion**\n",
    "\n",
    "DataFrame's work similar to dict objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    1.0\n",
      "b    2.0\n",
      "c    3.0\n",
      "d    NaN\n",
      "Name: one, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),\n",
    "    'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}\n",
    "df = pd.DataFrame(d)\n",
    "print (df['one'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   one  two  three\n",
      "a  1.0  1.0    1.0\n",
      "b  2.0  2.0    4.0\n",
      "c  3.0  3.0    9.0\n",
      "d  NaN  4.0    NaN\n"
     ]
    }
   ],
   "source": [
    "df['three']=df['one']*df['two']\n",
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   one  three\n",
      "a  1.0    1.0\n",
      "b  2.0    4.0\n",
      "c  3.0    9.0\n",
      "d  NaN    NaN\n"
     ]
    }
   ],
   "source": [
    "del df['two']\n",
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   one  three  foo\n",
      "a  1.0    1.0  bar\n",
      "b  2.0    4.0  bar\n",
      "c  3.0    9.0  bar\n",
      "d  NaN    NaN  bar\n"
     ]
    }
   ],
   "source": [
    "df['foo']='bar'\n",
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before\n",
      "   one  three  foo\n",
      "a  1.0    1.0  bar\n",
      "b  2.0    4.0  bar\n",
      "c  3.0    9.0  bar\n",
      "d  NaN    NaN  bar\n",
      "After\n",
      "   one  bar  three  foo\n",
      "a  1.0  1.0    1.0  bar\n",
      "b  2.0  2.0    4.0  bar\n",
      "c  3.0  3.0    9.0  bar\n",
      "d  NaN  NaN    NaN  bar\n"
     ]
    }
   ],
   "source": [
    "print (\"Before\")\n",
    "print (df)\n",
    "df.insert(1,\"bar\",df[\"one\"])\n",
    "print (\"After\")\n",
    "print (df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Indexing/Selection**\n",
    "\n",
    "<table>\n",
    "<tr><td>Operation</td><td>Syntax</td><td>Result</td></tr>\n",
    "<tr><td>Select column</td><td>df[col]</td><td>Series</td></tr>\n",
    "<tr><td>Select columns by labels</td><td>df[[list of labels]]</td><td>DataFrame</td></tr>\n",
    "<tr><td>Select row by index</td><td>df.loc[index]</td><td>Series</td></tr>\n",
    "<tr><td>Select row by integer location</td><td>df.iloc[loc]</td><td>Series</td></tr>\n",
    "<tr><td>Slice rows</td><td>df[5:10]</td><td>DataFrame</td></tr>\n",
    "<tr><td>Select rows by boolean vector</td><td>df[bool_vec]</td><td>DataFrame</td></tr>\n",
    "<tr><td> Flexible slicing</td><td>df.ix[rowselector, columnselector]</td><td>Variable</td></tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    2.0\n",
       "c    3.0\n",
       "d    NaN\n",
       "Name: one, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['one']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>bar</th>\n",
       "      <th>three</th>\n",
       "      <th>foo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  bar  three  foo\n",
       "c  3.0  3.0    9.0  bar\n",
       "d  NaN  NaN    NaN  bar"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  three\n",
       "a  1.0    1.0\n",
       "b  2.0    4.0\n",
       "c  3.0    9.0\n",
       "d  NaN    NaN"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"one\",\"three\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>bar</th>\n",
       "      <th>three</th>\n",
       "      <th>foo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  bar  three  foo\n",
       "c  3.0  3.0    9.0  bar\n",
       "d  NaN  NaN    NaN  bar"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[False,False,True,True]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   one  bar  three  foo\n",
      "a  1.0  1.0    1.0  bar\n",
      "b  2.0  2.0    4.0  bar\n",
      "c  3.0  3.0    9.0  bar\n",
      "d  NaN  NaN    NaN  bar\n",
      "Selecting row 'b'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "one        2\n",
       "bar        2\n",
       "three      4\n",
       "foo      bar\n",
       "Name: b, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (df)\n",
    "print (\"Selecting row 'b'\")\n",
    "df.loc['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   one  bar  three  foo\n",
      "a  1.0  1.0    1.0  bar\n",
      "b  2.0  2.0    4.0  bar\n",
      "c  3.0  3.0    9.0  bar\n",
      "d  NaN  NaN    NaN  bar\n"
     ]
    }
   ],
   "source": [
    "#flexible indexing via .ix method\n",
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   bar\n",
      "a  1.0\n",
      "b  2.0\n"
     ]
    }
   ],
   "source": [
    "print (df.ix[:2,1:2])#gives a data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    1.0\n",
      "b    2.0\n",
      "Name: bar, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print (df.ix[:2,1]) #gives a series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    1.0\n",
      "b    2.0\n",
      "Name: bar, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print (df.ix[:2,\"bar\"])#gives a sereies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   bar\n",
      "a  1.0\n",
      "b  2.0\n"
     ]
    }
   ],
   "source": [
    "print (df.ix[:2,[\"bar\"]])#gives a data frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Boolean Masking and where()**\n",
    "\n",
    "As in Numpy, we can use masks for accessing data elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       a  b         c\n",
      "0    one  x  0.397209\n",
      "1    one  y -1.467602\n",
      "2    two  y  0.570070\n",
      "3  three  x  0.720559\n",
      "4    two  y -0.554544\n",
      "5    one  x -0.308509\n",
      "6    six  x -0.733021\n"
     ]
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'a' : ['one','one','two','three','two','one','six'],\n",
    "                 'b' : ['x','y','y','x','y','x','x'],\n",
    "                 'c' : np.random.randn(7)})\n",
    "print (df2)\n",
    "criterion = df2['a'].map(lambda x: x.startswith('t')) #create a criterion using lambda function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    False\n",
      "1    False\n",
      "2     True\n",
      "3     True\n",
      "4     True\n",
      "5    False\n",
      "6    False\n",
      "Name: a, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "print (criterion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       a  b         c\n",
      "2    two  y  0.570070\n",
      "3  three  x  0.720559\n",
      "4    two  y -0.554544\n"
     ]
    }
   ],
   "source": [
    "print (df2[criterion])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>x</td>\n",
       "      <td>0.397209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>one</td>\n",
       "      <td>x</td>\n",
       "      <td>-0.308509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a  b         c\n",
       "0  one  x  0.397209\n",
       "5  one  x -0.308509"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#disjunction of two filter criterions using bit-and\n",
    "df2[(df2['a']=='one')&(df2['b']=='x')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     ids   ids2   vals\n",
      "0   True   True   True\n",
      "1   True  False  False\n",
      "2  False  False   True\n",
      "3  False  False  False\n"
     ]
    }
   ],
   "source": [
    "#check for several values over all columns with isin\n",
    "df3 = pd.DataFrame({'vals': [1, 2, 3, 4], \n",
    "                    'ids': ['a', 'b', 'f', 'n'],\n",
    "                    'ids2': ['a', 'n', 'c', 'n']})\n",
    "\n",
    "values = ['a', 'b', 1, 3]\n",
    "print (df3.isin(values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.397209\n",
      "1   -1.467602\n",
      "2    0.570070\n",
      "3    0.720559\n",
      "4   -0.554544\n",
      "5   -0.308509\n",
      "6   -0.733021\n",
      "Name: c, dtype: float64\n",
      "0    0.397209\n",
      "1         NaN\n",
      "2    0.570070\n",
      "3    0.720559\n",
      "4         NaN\n",
      "5         NaN\n",
      "6         NaN\n",
      "Name: c, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#with where we can guarantee the the shape stays the same\n",
    "s=df2['c']\n",
    "print (s)\n",
    "print (s.where(s>0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Data alignment and arithmetic**\n",
    "\n",
    "DataFrame objects automatically align on both the columns and the index (row labels). Again, the resulting object will have the union of the column and row labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          A         B         C   D\n",
      "0 -2.397290  0.053219 -0.560677 NaN\n",
      "1  1.021603 -0.297019  0.548527 NaN\n",
      "2 -0.349425  1.610391  1.738908 NaN\n",
      "3 -1.234902  0.076946 -1.662344 NaN\n",
      "4 -1.572823 -0.686018  0.662440 NaN\n",
      "5  0.608798 -1.111543 -1.354657 NaN\n",
      "6  1.582757 -0.395040  0.761686 NaN\n",
      "7       NaN       NaN       NaN NaN\n",
      "8       NaN       NaN       NaN NaN\n",
      "9       NaN       NaN       NaN NaN\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(10, 4), columns=['A', 'B', 'C', 'D']) \n",
    "df2 = pd.DataFrame(np.random.randn(7, 3), columns=['A', 'B', 'C'])\n",
    "print (df + df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "** Broadcasting behaviour**\n",
    "\n",
    "When doing an operation between DataFrame and Series, the default behavior is to align the Series index on the DataFrame columns, thus broadcasting row-wise. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          A         B         C         D\n",
      "0 -1.193378  0.362220 -0.185914  1.408487\n",
      "1  0.713218  0.052878 -0.090530 -1.129024\n",
      "2 -1.014749  0.412556  0.382431  0.740610\n",
      "3 -2.029758 -0.486587 -1.354926 -0.115527\n",
      "4 -0.846842 -0.588906  0.464537 -1.063856\n",
      "5  1.096341 -1.216381 -0.926981 -1.840147\n",
      "6 -0.573001  0.452942 -0.264215 -0.126698\n",
      "7  0.382752  1.332689  1.002710  1.080878\n",
      "8 -0.028819  0.372059  1.438581 -1.367700\n",
      "9 -0.605105  0.527056  0.017148 -1.712500\n",
      "==========================================\n",
      "A   -1.193378\n",
      "B    0.362220\n",
      "C   -0.185914\n",
      "D    1.408487\n",
      "Name: 0, dtype: float64\n",
      "==========================================\n",
      "          A         B         C         D\n",
      "0  0.000000  0.000000  0.000000  0.000000\n",
      "1  1.906595 -0.309343  0.095384 -2.537511\n",
      "2  0.178629  0.050336  0.568345 -0.667877\n",
      "3 -0.836381 -0.848807 -1.169012 -1.524014\n",
      "4  0.346535 -0.951127  0.650451 -2.472343\n",
      "5  2.289719 -1.578602 -0.741067 -3.248634\n",
      "6  0.620377  0.090721 -0.078300 -1.535185\n",
      "7  1.576130  0.970468  1.188624 -0.327609\n",
      "8  1.164559  0.009838  1.624495 -2.776186\n",
      "9  0.588272  0.164836  0.203062 -3.120987\n"
     ]
    }
   ],
   "source": [
    "print (df)\n",
    "print (\"==========================================\")\n",
    "print (df.iloc[0])\n",
    "print (\"==========================================\")\n",
    "print (df-df.iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.386755</td>\n",
       "      <td>0.724440</td>\n",
       "      <td>-0.371828</td>\n",
       "      <td>2.816974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.426435</td>\n",
       "      <td>0.105755</td>\n",
       "      <td>-0.181060</td>\n",
       "      <td>-2.258048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.029498</td>\n",
       "      <td>0.825112</td>\n",
       "      <td>0.764862</td>\n",
       "      <td>1.481221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-4.059516</td>\n",
       "      <td>-0.973174</td>\n",
       "      <td>-2.709852</td>\n",
       "      <td>-0.231054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.693685</td>\n",
       "      <td>-1.177813</td>\n",
       "      <td>0.929074</td>\n",
       "      <td>-2.127712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.192683</td>\n",
       "      <td>-2.432763</td>\n",
       "      <td>-1.853962</td>\n",
       "      <td>-3.680294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.146001</td>\n",
       "      <td>0.905883</td>\n",
       "      <td>-0.528429</td>\n",
       "      <td>-0.253396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.765504</td>\n",
       "      <td>2.665377</td>\n",
       "      <td>2.005420</td>\n",
       "      <td>2.161757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.057637</td>\n",
       "      <td>0.744117</td>\n",
       "      <td>2.877161</td>\n",
       "      <td>-2.735399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.210211</td>\n",
       "      <td>1.054112</td>\n",
       "      <td>0.034296</td>\n",
       "      <td>-3.425001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0 -2.386755  0.724440 -0.371828  2.816974\n",
       "1  1.426435  0.105755 -0.181060 -2.258048\n",
       "2 -2.029498  0.825112  0.764862  1.481221\n",
       "3 -4.059516 -0.973174 -2.709852 -0.231054\n",
       "4 -1.693685 -1.177813  0.929074 -2.127712\n",
       "5  2.192683 -2.432763 -1.853962 -3.680294\n",
       "6 -1.146001  0.905883 -0.528429 -0.253396\n",
       "7  0.765504  2.665377  2.005420  2.161757\n",
       "8 -0.057637  0.744117  2.877161 -2.735399\n",
       "9 -1.210211  1.054112  0.034296 -3.425001"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#broadcasting with scalars as in numpy\n",
    "df*2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "When working with time series (`TimeSeries`) broadcasting is done column wise. However this function is **deprecated**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C\n",
      "2000-01-01  0.510118  3.889524  1.923528\n",
      "2000-01-02  1.475033  1.904546  0.130995\n",
      "2000-01-03 -1.617337  0.054794  0.989100\n",
      "2000-01-04  0.531948  0.064584  1.184315\n",
      "2000-01-05  0.073592 -0.258974 -2.469773\n",
      "2000-01-06  0.425837 -0.821244 -1.563358\n",
      "2000-01-07  0.362407  0.265029  1.022454\n",
      "2000-01-08  1.488831 -0.257652 -0.040991\n"
     ]
    }
   ],
   "source": [
    "index = pd.date_range('1/1/2000', periods=8)\n",
    "df = pd.DataFrame(np.random.randn(8, 3), index=index, columns=list('ABC'))\n",
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            2000-01-01 00:00:00  2000-01-02 00:00:00  2000-01-03 00:00:00  \\\n",
      "2000-01-01                  NaN                  NaN                  NaN   \n",
      "2000-01-02                  NaN                  NaN                  NaN   \n",
      "2000-01-03                  NaN                  NaN                  NaN   \n",
      "2000-01-04                  NaN                  NaN                  NaN   \n",
      "2000-01-05                  NaN                  NaN                  NaN   \n",
      "2000-01-06                  NaN                  NaN                  NaN   \n",
      "2000-01-07                  NaN                  NaN                  NaN   \n",
      "2000-01-08                  NaN                  NaN                  NaN   \n",
      "\n",
      "            2000-01-04 00:00:00  2000-01-05 00:00:00  2000-01-06 00:00:00  \\\n",
      "2000-01-01                  NaN                  NaN                  NaN   \n",
      "2000-01-02                  NaN                  NaN                  NaN   \n",
      "2000-01-03                  NaN                  NaN                  NaN   \n",
      "2000-01-04                  NaN                  NaN                  NaN   \n",
      "2000-01-05                  NaN                  NaN                  NaN   \n",
      "2000-01-06                  NaN                  NaN                  NaN   \n",
      "2000-01-07                  NaN                  NaN                  NaN   \n",
      "2000-01-08                  NaN                  NaN                  NaN   \n",
      "\n",
      "            2000-01-07 00:00:00  2000-01-08 00:00:00   A   B   C  \n",
      "2000-01-01                  NaN                  NaN NaN NaN NaN  \n",
      "2000-01-02                  NaN                  NaN NaN NaN NaN  \n",
      "2000-01-03                  NaN                  NaN NaN NaN NaN  \n",
      "2000-01-04                  NaN                  NaN NaN NaN NaN  \n",
      "2000-01-05                  NaN                  NaN NaN NaN NaN  \n",
      "2000-01-06                  NaN                  NaN NaN NaN NaN  \n",
      "2000-01-07                  NaN                  NaN NaN NaN NaN  \n",
      "2000-01-08                  NaN                  NaN NaN NaN NaN  \n"
     ]
    }
   ],
   "source": [
    "print (df - df['A'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Transposing a Data Frame**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C\n",
      "2000-01-01  0.510118  3.889524  1.923528\n",
      "2000-01-02  1.475033  1.904546  0.130995\n",
      "2000-01-03 -1.617337  0.054794  0.989100\n",
      "2000-01-04  0.531948  0.064584  1.184315\n",
      "2000-01-05  0.073592 -0.258974 -2.469773\n",
      "   2000-01-01  2000-01-02  2000-01-03  2000-01-04  2000-01-05\n",
      "A    0.510118    1.475033   -1.617337    0.531948    0.073592\n",
      "B    3.889524    1.904546    0.054794    0.064584   -0.258974\n",
      "C    1.923528    0.130995    0.989100    1.184315   -2.469773\n"
     ]
    }
   ],
   "source": [
    "print (df[:5])\n",
    "print (df[:5].T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Compatibility with Numpy**\n",
    "\n",
    "Given numeric values, numpy element wise functions can be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C\n",
      "2000-01-01  0.510118  3.889524  1.923528\n",
      "2000-01-02  1.475033  1.904546  0.130995\n",
      "2000-01-03 -1.617337  0.054794  0.989100\n",
      "2000-01-04  0.531948  0.064584  1.184315\n",
      "2000-01-05  0.073592 -0.258974 -2.469773\n",
      "2000-01-06  0.425837 -0.821244 -1.563358\n",
      "2000-01-07  0.362407  0.265029  1.022454\n",
      "2000-01-08  1.488831 -0.257652 -0.040991\n",
      "                   A          B         C\n",
      "2000-01-01  1.665488  48.887623  6.845063\n",
      "2000-01-02  4.371179   6.716359  1.139962\n",
      "2000-01-03  0.198426   1.056323  2.688814\n",
      "2000-01-04  1.702246   1.066715  3.268448\n",
      "2000-01-05  1.076367   0.771843  0.084604\n",
      "2000-01-06  1.530871   0.439884  0.209432\n",
      "2000-01-07  1.436783   1.303469  2.780009\n",
      "2000-01-08  4.431913   0.772864  0.959838\n"
     ]
    }
   ],
   "source": [
    "print (df)\n",
    "print (np.exp(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 3 Basic Functionality\n",
    "\n",
    "* Accessing head and tails via `DataFrame.head` and `DataFrame.tail`\n",
    "* Explicit arithmetic operations:  `DataFrame.add`, `DataFrame.sub` etc. allow to control broadcasting and value filling\n",
    "* DataFrame comparators: `DataFrame.gt`, `DataFrame.ne` etc.\n",
    "* Boolean reductions: `DataFrame.all()`, `DataFrame.any()``\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 3.1 Head and Tail\n",
    "\n",
    "Viewing long series/data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0   -0.653285\n",
      "1   -0.865533\n",
      "2   -0.361412\n",
      "3   -0.220708\n",
      "4    0.225272\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "long_series = pd.Series(np.random.randn(1000))\n",
    "print (long_series.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "995    1.541859\n",
      "996    0.291662\n",
      "997   -0.961986\n",
      "998    0.101844\n",
      "999    0.234632\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print (long_series.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     -0.653285\n",
      "1     -0.865533\n",
      "2     -0.361412\n",
      "3     -0.220708\n",
      "4      0.225272\n",
      "5     -1.416084\n",
      "6      0.100536\n",
      "7      1.821810\n",
      "8     -0.025045\n",
      "9     -0.455368\n",
      "10     0.971690\n",
      "11    -1.701802\n",
      "12     2.788344\n",
      "13     0.186438\n",
      "14    -1.563145\n",
      "15     0.582845\n",
      "16    -0.834318\n",
      "17    -0.506008\n",
      "18     0.015123\n",
      "19     0.847408\n",
      "20    -0.131787\n",
      "21    -0.550609\n",
      "22    -1.027453\n",
      "23     0.848934\n",
      "24    -0.691227\n",
      "25     0.451914\n",
      "26     0.491481\n",
      "27     0.291125\n",
      "28    -0.613710\n",
      "29     1.689074\n",
      "         ...   \n",
      "970    0.383880\n",
      "971   -0.229128\n",
      "972    0.854397\n",
      "973   -0.408504\n",
      "974   -0.213414\n",
      "975    0.218063\n",
      "976   -0.701190\n",
      "977   -0.607696\n",
      "978    1.895958\n",
      "979   -0.619735\n",
      "980    0.135332\n",
      "981    1.017930\n",
      "982    0.814764\n",
      "983    0.848620\n",
      "984   -0.051340\n",
      "985    1.280587\n",
      "986   -0.178101\n",
      "987   -1.042671\n",
      "988    2.137616\n",
      "989    1.389060\n",
      "990   -0.172996\n",
      "991    0.225781\n",
      "992   -0.256933\n",
      "993    0.495566\n",
      "994   -0.924152\n",
      "995    1.541859\n",
      "996    0.291662\n",
      "997   -0.961986\n",
      "998    0.101844\n",
      "999    0.234632\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print (long_series)# see reduce printing behaviour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#4. Descriptive Statistics\n",
    "\n",
    "Descriptive Statistics sumarizes the underlying distribution of data values through statistical values like mean, variance etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "##4.1 Basic Functions\n",
    "\n",
    "<table>\n",
    "<tr><td>Function</td><td>Description</td>\n",
    "<tr><td>count</td><td>Number of non-null observations</td>\n",
    "<tr><td>sum</td><td>Sum of values</td>\n",
    "<tr><td>mean</td><td>Mean of values</td>\n",
    "<tr><td>mad</td><td>Mean absolute deviation</td>\n",
    "<tr><td>median</td><td>Arithmetic median of values</td>\n",
    "<tr><td>min</td><td>Minimum</td>\n",
    "<tr><td>max</td><td>Maximum</td>\n",
    "<tr><td>mode</td><td>Mode</td>\n",
    "<tr><td>abs</td><td>Absolute Value</td>\n",
    "<tr><td>prod</td><td>Product of values</td>\n",
    "<tr><td>std</td><td>Unbiased standard deviation</td>\n",
    "<tr><td>var</td><td>Unbiased variance</td>\n",
    "<tr><td>skew</td><td>Unbiased skewness (3rd moment)</td>\n",
    "<tr><td>kurt</td><td>Unbiased kurtosis (4th moment)</td>\n",
    "<tr><td>quantile</td><td>Sample quantile (value at %)</td>\n",
    "<tr><td>cumsum</td><td>Cumulative sum</td>\n",
    "<tr><td>cumprod</td><td>Cumulative product</td>\n",
    "<tr><td>cummax</td><td>Cumulative maximum</td>\n",
    "<tr><td>cummin</td><td>Cumulative minimum</td>\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C\n",
      "2000-01-01  0.510118  3.889524  1.923528\n",
      "2000-01-02  1.475033  1.904546  0.130995\n",
      "2000-01-03 -1.617337  0.054794  0.989100\n",
      "2000-01-04  0.531948  0.064584  1.184315\n",
      "2000-01-05  0.073592 -0.258974 -2.469773\n",
      "2000-01-06  0.425837 -0.821244 -1.563358\n",
      "2000-01-07  0.362407  0.265029  1.022454\n",
      "2000-01-08  1.488831 -0.257652 -0.040991\n"
     ]
    }
   ],
   "source": [
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A    1.488831\n",
      "B    3.889524\n",
      "C    1.923528\n",
      "dtype: float64\n",
      "========================================\n",
      "2000-01-01    3.889524\n",
      "2000-01-02    1.904546\n",
      "2000-01-03    0.989100\n",
      "2000-01-04    1.184315\n",
      "2000-01-05    0.073592\n",
      "2000-01-06    0.425837\n",
      "2000-01-07    1.022454\n",
      "2000-01-08    1.488831\n",
      "Freq: D, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#some examples\n",
    "print (df.max())\n",
    "print (\"========================================\")\n",
    "print (df.max(axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "##4.2 Describe\n",
    "\n",
    "`describe` summarizes a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.406304</td>\n",
       "      <td>0.605076</td>\n",
       "      <td>0.147034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.967230</td>\n",
       "      <td>1.545134</td>\n",
       "      <td>1.488818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.617337</td>\n",
       "      <td>-0.821244</td>\n",
       "      <td>-2.469773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.290203</td>\n",
       "      <td>-0.257983</td>\n",
       "      <td>-0.421583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.467978</td>\n",
       "      <td>0.059689</td>\n",
       "      <td>0.560048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.767719</td>\n",
       "      <td>0.674908</td>\n",
       "      <td>1.062919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.488831</td>\n",
       "      <td>3.889524</td>\n",
       "      <td>1.923528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C\n",
       "count  8.000000  8.000000  8.000000\n",
       "mean   0.406304  0.605076  0.147034\n",
       "std    0.967230  1.545134  1.488818\n",
       "min   -1.617337 -0.821244 -2.469773\n",
       "25%    0.290203 -0.257983 -0.421583\n",
       "50%    0.467978  0.059689  0.560048\n",
       "75%    0.767719  0.674908  1.062919\n",
       "max    1.488831  3.889524  1.923528"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 4.3 Index  of min and max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   A         B         C\n",
      "2000-01-01  0.510118  3.889524  1.923528\n",
      "2000-01-02  1.475033  1.904546  0.130995\n",
      "2000-01-03 -1.617337  0.054794  0.989100\n",
      "2000-01-04  0.531948  0.064584  1.184315\n",
      "2000-01-05  0.073592 -0.258974 -2.469773\n",
      "2000-01-06  0.425837 -0.821244 -1.563358\n",
      "2000-01-07  0.362407  0.265029  1.022454\n",
      "2000-01-08  1.488831 -0.257652 -0.040991\n"
     ]
    }
   ],
   "source": [
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A   2000-01-08\n",
      "B   2000-01-01\n",
      "C   2000-01-01\n",
      "dtype: datetime64[ns]\n",
      "============================\n",
      "A   2000-01-03\n",
      "B   2000-01-06\n",
      "C   2000-01-05\n",
      "dtype: datetime64[ns]\n",
      "============================\n",
      "2000-01-01    B\n",
      "2000-01-02    B\n",
      "2000-01-03    C\n",
      "2000-01-04    C\n",
      "2000-01-05    A\n",
      "2000-01-06    A\n",
      "2000-01-07    C\n",
      "2000-01-08    A\n",
      "Freq: D, dtype: object\n",
      "============================\n",
      "2000-01-01    A\n",
      "2000-01-02    C\n",
      "2000-01-03    A\n",
      "2000-01-04    B\n",
      "2000-01-05    C\n",
      "2000-01-06    C\n",
      "2000-01-07    B\n",
      "2000-01-08    B\n",
      "Freq: D, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print (df.idxmax(axis=0))\n",
    "print (\"============================\")\n",
    "print (df.idxmin(axis=0))\n",
    "print (\"============================\")\n",
    "print (df.idxmax(axis=1))\n",
    "print (\"============================\")\n",
    "print (df.idxmin(axis=1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "##4.4 Histogram\n",
    "\n",
    "`value_counts()`allows to count the histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6    9\n",
      "4    9\n",
      "5    8\n",
      "2    7\n",
      "3    6\n",
      "0    6\n",
      "1    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "data = np.random.randint(0, 7, size=50)\n",
    "s = pd.Series(data)\n",
    "print (s.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 5 Group By\n",
    "\n",
    "Pandas supports grouping data frames for particular columns similar to a SQL group by clause."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method groupby in module pandas.core.generic:\n",
      "\n",
      "groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs) method of pandas.core.frame.DataFrame instance\n",
      "    Group series using mapper (dict or key function, apply given function\n",
      "    to group, return result as series) or by a series of columns.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    by : mapping function / list of functions, dict, Series, or tuple /\n",
      "        list of column names.\n",
      "        Called on each element of the object index to determine the groups.\n",
      "        If a dict or Series is passed, the Series or dict VALUES will be\n",
      "        used to determine the groups\n",
      "    axis : int, default 0\n",
      "    level : int, level name, or sequence of such, default None\n",
      "        If the axis is a MultiIndex (hierarchical), group by a particular\n",
      "        level or levels\n",
      "    as_index : boolean, default True\n",
      "        For aggregated output, return object with group labels as the\n",
      "        index. Only relevant for DataFrame input. as_index=False is\n",
      "        effectively \"SQL-style\" grouped output\n",
      "    sort : boolean, default True\n",
      "        Sort group keys. Get better performance by turning this off.\n",
      "        Note this does not influence the order of observations within each\n",
      "        group.  groupby preserves the order of rows within each group.\n",
      "    group_keys : boolean, default True\n",
      "        When calling apply, add group keys to index to identify pieces\n",
      "    squeeze : boolean, default False\n",
      "        reduce the dimensionality of the return type if possible,\n",
      "        otherwise return a consistent type\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    DataFrame results\n",
      "    \n",
      "    >>> data.groupby(func, axis=0).mean()\n",
      "    >>> data.groupby(['col1', 'col2'])['col3'].mean()\n",
      "    \n",
      "    DataFrame with hierarchical index\n",
      "    \n",
      "    >>> data.groupby(['col1', 'col2']).mean()\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    GroupBy object\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(df.groupby)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       a  b         c\n",
      "0    one  x -1.928164\n",
      "1    one  y -1.543005\n",
      "2    two  y -1.612611\n",
      "3  three  x -0.281879\n",
      "4    two  y -0.776053\n",
      "5    one  x  0.491386\n",
      "6    six  x  0.189380\n"
     ]
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'a' : ['one','one','two','three','two','one','six'],\n",
    "                 'b' : ['x','y','y','x','y','x','x'],\n",
    "                 'c' : np.random.randn(7)})\n",
    "print (df2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pandas.core.groupby.DataFrameGroupBy object at 0x7fcb6e237390>\n"
     ]
    }
   ],
   "source": [
    "print (df2.groupby('a')) #creates a groupby object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              c\n",
      "a              \n",
      "one   -0.993261\n",
      "six    0.189380\n",
      "three -0.281879\n",
      "two   -1.194332\n"
     ]
    }
   ],
   "source": [
    "#gropuing requires an aggregate function\n",
    "print (df2.groupby('a').mean()) #creates a groupby object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                c\n",
      "a     b          \n",
      "one   x -0.718389\n",
      "      y -1.543005\n",
      "six   x  0.189380\n",
      "three x -0.281879\n",
      "two   y -1.194332\n"
     ]
    }
   ],
   "source": [
    "#works on multiple levels\n",
    "print (df2.groupby(['a','b']).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 6. IO Tools\n",
    "\n",
    "The Pandas I/O api is a set of top level reader functions accessed like `pd.read_csv()` that generally return a pandas object.\n",
    "* read_csv\n",
    "* read_excel\n",
    "* read_hdf\n",
    "* read_sql\n",
    "* read_json\n",
    "* read_msgpack (experimental) \n",
    "* read_html\n",
    "* read_gbq (experimental)\n",
    "* read_stata\n",
    "* read_clipboard\n",
    "* read_pickle\n",
    "\n",
    "The corresponding writer functions are object methods that are accessed like df.to_csv() • to_csv\n",
    "* to_excel\n",
    "* to_hdf\n",
    "* to_sql\n",
    "* to_json\n",
    "* to_msgpack (experimental) • to_html\n",
    "* to_gbq (experimental) • to_stata\n",
    "* to_clipboard\n",
    "* to_pickle\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "** Open Data Access **\n",
    "\n",
    "Further, pandas supports some open data APIs, namely\n",
    "\n",
    "* Yahoo! Finance\n",
    "* Google Finance\n",
    "* St. Louis FED (FRED)\n",
    "* Kenneth French’s data library \n",
    "* World Bank"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#6.1 Loading the Weather Data from the CSV\n",
    "\n",
    "In this example we load the weather datafrom the data directory ( \"data\\weather_data.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iris.dat  stockholm_td_adj.dat\tweather_data.csv\r\n"
     ]
    }
   ],
   "source": [
    "#! executes a shell command\n",
    "!ls data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Day,outlook,temperature,humidity,windy,play\r\n",
      "1,sunny,85,85,false,no\r\n",
      "2,sunny,80,90,true,no\r\n",
      "3,overcast,83,86,false,yes\r\n",
      "4,rainy,70,96,false,yes\r\n",
      "5,rainy,68,80,false,yes\r\n",
      "6,rainy,65,70,true,no\r\n",
      "7,overcast,64,65,true,yes\r\n"
     ]
    }
   ],
   "source": [
    "!cat data/weather_data.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Day   outlook  temperature  humidity  windy play\n",
      "0    1     sunny           85        85  False   no\n",
      "1    2     sunny           80        90   True   no\n",
      "2    3  overcast           83        86  False  yes\n",
      "3    4     rainy           70        96  False  yes\n",
      "4    5     rainy           68        80  False  yes\n",
      "5    6     rainy           65        70   True   no\n",
      "6    7  overcast           64        65   True  yes\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/weather_data.csv\")\n",
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "#use help to see the parameters \n",
    "pd.read_csv?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Exercise 6.1. Analysing New York City Open Data with Pandas\n",
    "\n",
    "Go to the exercise folder and conduct exercise 6.1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 7 Visualisations with Pandas\n",
    "\n",
    "Pandas has a number of build in visualisations using data frames.\n",
    "\n",
    "The default visualisations are build on top of mathplotlib (see  [Mathplotlib in a Nutshell](http://nbviewer.ipython.org/urls/raw.github.com/mgrani/LODA-lecture-notes-on-data-analysis/master/1.SP-in-Python/SPiP-5-Matplotlib.ipynb) for an introduction to Mathplotlib)\n",
    "\n",
    "Hence, `mathplotlib` has to be imported"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2881: FutureWarning: \n",
      "mpl_style had been deprecated and will be removed in a future version.\n",
      "Use `matplotlib.pyplot.style.use` instead.\n",
      "\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "#to enable nicer plots\n",
    "pd.options.display.mpl_style = 'default'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "#and enable to include plots inline in HTML\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 7.1 Basic Plotting: using `plot``\n",
    "\n",
    "The plot method on Series and DataFrame is just a simple wrapper around `plt.plot()`from the mathplotlib library\n",
    "￼￼"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['monospace'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEDCAYAAAA/eB+kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb6b8535c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))\n",
    "ts = ts.cumsum() #cumulative sum\n",
    "ts.plot()\n",
    "ts.cumsum?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb6821cba8>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['monospace'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEDCAYAAAA/eB+kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb6829be10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting a dataframe\n",
    "df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))\n",
    "df=df.cumsum() #cumulative sum to make the plot visually more robust\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb6809a9b0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['monospace'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEICAYAAABYoZ8gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nslvphOAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb680b6860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['A']=np.abs(df['A']).cumsum() # make A ever increasing\n",
    "df.plot(x='A',y='B') #Plot A vs. B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#7.2. Changing the plot `kind`\n",
    "\n",
    "The keyword argument `kind` of the plot function allows to change the plot type.\n",
    "\n",
    "Available types are:\n",
    "\n",
    "* `bar` or  `barh` for bar plots\n",
    "* `kde` or `density` for density plots\n",
    "* `area` for area plots\n",
    "* `scatter` for scater plots\n",
    "* `hexbin` for hexagonal bin plots\n",
    "* `pie` for pie pots\n",
    "\n",
    "In addition to these `kind`'s, there are the `DataFrame.hist()`, and `DataFrame.boxplot()` methods, which use a separate interface.\n",
    "\n",
    "Finally, there are several plotting functions in `pandas.tools.plotting`:\n",
    "* Scatter Matrix\n",
    "* Andrews Curves\n",
    "* Parallel Coordinates\n",
    "* Lag Plot\n",
    "* Autocorrelation Plot\n",
    "* Bootstrap Plot\n",
    "* RadViz\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Bar Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7fcb6823f6d8>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['monospace'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAD1CAYAAACm0cXeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADChJREFUeJzt3XuIpXd9x/H3yaUmsRYNRiMqqI2KIag5eImstMZJw7au\nBm9fjZfgZQXBlEaU4LpgSkVdEFpFg6VuS1sN6tfLGtCg645KEFRMnhWjbBAviPFCaheN7RFDzPjH\njrhuZuacM8/vzLPfnfcLFvY8Z+b7fJ/fOXzOb35zzvxGKysrSJJqOm3oBiRJm2eIS1JhhrgkFWaI\nS1JhhrgkFWaIS1JhZ2z1CZeXl31PoyRtwtLS0ujEY1se4gDj8XiI086l67oSfVbheLbjWLZVZTy7\nrlvzuMspklSYIS5JhRniklSYIS5JhRniklSYIS5JhRniklSYIS5JhQ3yYZ/WLt9/eAFVR9C1rXtw\n98VN60mSM3FJKswQl6TCDHFJKswQl6TCmv1iMyJOB24BfpKZu1rVlSStr+VM/B+AIw3rSZKmaBLi\nEfEI4DnA/hb1JEmzaTUTfw9wLXBvo3qSpBn0XhOPiF3AnZl5a0Q8a5bvWW+His27z45FJ6X2113H\nZDLZ1tffkmPZVvXxbPGLzR3A8yLi74CzgL+IiA9n5ivW+4bmWyE1/mTlolTYAmpRqmyBVYFj2VaV\n8VzvhaZ3iGfmHmAPwOpM/M0bBbgkqR3fJy5JhTX9A1iZ+WXgyy1rSpLW50xckgozxCWpMENckgoz\nxCWpMENckgozxCWpMENckgozxCWpMENckgozxCWpMENckgozxCWpMENckgozxCWpsKZ/ilbSn7p8\n/yJ2nRo1383q4O6Lm9bT1nEmLkmFtdgo+SzgZuB+q/U+kZnX9a0rSZquxUz8t8CzM/NJwJOBnRFx\nSYO6kqQpWmyUvAL83+rNM1f/rfStK0marskvNiPidOBW4ALg+sz8eou6kqSNjVZW2k2aI+KBwAHg\n7zPz22t9zfLycvNZ+lu6UeuSC7FvvH1/QJlMJpxzzjlDt7HlfG6e/Co9N5eWlu7zhGq92/0vI+JL\nwE5gzRAHGI/HLU/b/O1Wi9L8ugvpum57Xr/PzZNeledm13VrHu/9i82IOG91Bk5EnA38DXB737qS\npOlazMQfBvzX6rr4aUBm5mca1JUkTdHi3SnfAvy4lyQNwE9sSlJhhrgkFWaIS1JhhrgkFWaIS1Jh\nhrgkFWaIS1JhhrgkFWaIS1JhhrgkFWaIS1JhhrgkFWaIS1JhhrgkFWaIS1JhhrgkFdZ7U4iIeCTw\n38BDgRXg3zLzvX3rSpKmazETvwd4U2ZeCFwCvCEiLmxQV5I0Re8Qz8yfZWa3+v9fA0eAh/etK0ma\nrumaeEQ8imP7bX69ZV1J0tpa7HYPQET8OfBJ4JrMvGujr+26rtVpV40a11uM9te9GG/pFjGeI+gO\nN6u2b7zSrNZi+dw82U0mk9LX3yTEI+JMjgX4DZn5qWlfPx6PW5z2jxqGwyI1v+5FKTCejmVbZcZz\nAbquK3H9673Q9F5OiYgR8O/Akcz85771JEmzazET3wG8ErgtIr65euytmXlTg9qSpA30DvHM/ApV\nFv4k6RTjJzYlqTBDXJIKM8QlqTBDXJIKM8QlqTBDXJIKM8QlqTBDXJIKM8QlqTBDXJIKM8QlqTBD\nXJIKM8QlqTBDXJIKM8QlqTBDXJIKa7XH5n8Au4A7M/OiFjUlSdO1mon/J7CzUS1J0oyahHhm3gwc\nbVFLkjQ718QlqbAma+Lz6rquccUa+zS3v+5FOfnH07Fsq854tjeZTEpf/yAhPh6P2xbsDrettyDN\nr3tRCoynY9lWmfFcgK7rSlz/ei80LqdIUmFNQjwiPgJ8FXh8RNwREa9tUVeStLEmyymZeWWLOpKk\n+bicIkmFGeKSVJghLkmFGeKSVJghLkmFGeKSVJghLkmFGeKSVJghLkmFGeKSVJghLkmFGeKSVJgh\nLkmFGeKSVJghLkmFGeKSVJghLkmFNdnZJyJ2Au8FTgf2Z+a+FnUlSRvrPROPiNOB64G/BS4EroyI\nC/vWlSRN12I55WnA9zLzB5l5N/BR4IoGdSVJU7RYTnk48OPjbt8BPH2jbzj33HMbnLaec68duoNT\nh2PZluN58jt06NCax5usic/r6NGjQ5x2Ll3XMR6Ph27jlOF4trOdx/Ly/YeHbmEmB3df3Lxm13Vr\nHm+xnPIT4JHH3X7E6jFJ0oK1mIl/A3hsRDyaY+H9UuBlDepKkqboPRPPzHuAq4HPA0eOHcrv9K0r\nSZquyZp4Zt4E3NSiliRpdn5iU5IKM8QlqTBDXJIKM8QlqTBDXJIKM8QlqTBDXJIKM8QlqTBDXJIK\nM8QlqTBDXJIKM8QlqTBDXJIKM8QlqTBDXJIKM8QlqbBem0JExIuBfwSeADwtM29p0ZQkaTZ9Z+Lf\nBl4A3NygF0nSnHrNxDPzCEBEtOlGkjSXJntszqvruiFOO5fJZFKizyocz3a291iOhm5gJlv5+EwN\n8Yg4BJy/xl17M/PGzZx0PB5v5tu2VNd1JfqswvFsZ1uPZXd46A5msojHZ70XhqkhnpmXNe9GktSE\nbzGUpMJ6hXhEPD8i7gCeAXw2Ij7fpi1J0iz6vjvlAHCgUS+SpDm5nCJJhRniklSYIS5JhRniklSY\nIS5JhRniklSYIS5JhRniklSYIS5JhRniklSYIS5JhRniklSYIS5JhRniklSYIS5JhRniklRYr00h\nIuLdwHOBu4HvA6/OzF+2aEySNF3fmfgXgIsy84nAd4E9/VuSJM2q7/ZsB4+7+TXgRf3akSTNo1eI\nn+A1wMdm+cKu6xqedjEmk0mJPqtwPNvZ3mM5GrqBmWzl4zM1xCPiEHD+GnftzcwbV79mL3APcMMs\nJx2Px/P0OIiu60r0WYXj2c62Hsvu8NAdzGQRj896LwxTQzwzL9vo/oh4FbALWMrMlc00J0nanL7v\nTtkJXAv8dWZO2rQkSZpV33envB94APCFiPhmRPxrg54kSTPq++6UC1o1Ikman5/YlKTCDHFJKswQ\nl6TCDHFJKswQl6TCDHFJKswQl6TCDHFJKswQl6TCDHFJKswQl6TCDHFJKswQl6TCDHFJKswQl6TC\nDHFJKqzv9mxvB64A7gXuBF6VmT9t0Zgkabq+M/F3Z+YTM/PJwGeAtzXoSZI0o14hnpl3HXfz/oC7\n3UvSFuq1nAIQEe8ArgJ+BVw6y/d0Xdf3tAs3mUxK9FmF49nO9h7L0dANzGQrH5+pIR4Rh4Dz17hr\nb2bemJl7gb0RsQe4GrhuWs3xeDx3o1ut67oSfVbheLazrceyOzx0BzNZxOOz3gvD1BDPzMtmPMcN\nwE3MEOKSpDZ6rYlHxGOPu3kFcHu/diRJ8+i7Jr4vIh7PsbcY/gh4ff+WJEmz6hXimfnCVo1Ikubn\nJzYlqTBDXJIKM8QlqTBDXJIKM8QlqTBDXJIKM8QlqTBDXJIKM8QlqTBDXJIKM8QlqTBDXJIKM8Ql\nqTBDXJIKM8QlqTBDXJIKM8QlqbDRysrKlp5weXl5a08oSaeIpaWl0YnHtjzEJUntuJwiSYUZ4pJU\nmCEuSYUZ4pJUmCEuSYUZ4pJUmCG+joh4ZkRcP3Qf2t4i4oKI2LHG8R0R8ZdD9HSqiIjzIuK8ofvo\n64yhGziZRMTFwMuAFwM/BD41bEenjoh4MPC/mekHE+bzHmDPGsfvWr3vuVvbTm0RMQKuA67m2CR2\nFBH3AO/LzH8atLlN2vYhHhGPA65c/fcL4GPAKDMvHbSxwiLiEmAfcBR4O/Ah4MHAaRFxVWZ+bsj+\ninloZt524sHMvC0iHjVAP9W9EdgBPDUzfwgQEY8BPhARb8zMfxm0u01wOQVuB54N7MrMZ2bm+4Df\nDdxTde8H3gl8BPgisDszzwf+CnjXkI0V9MAN7jt7y7o4dbwSuPIPAQ6QmT8AXgFcNVhXPRji8ALg\nZ8CXIuKDEbEE3OfvE2guZ2Tmwcz8OPDzzPwaQGbePnBfFd0SEa878WBE7AZuHaCf6s7MzF+ceDAz\n/wc4c4B+etv2yymZ+Wng0xFxf+AK4BrgIRHxAeBAZh4ctMGa7j3u/7854T7XxOdzDXAgIl7OH0P7\nKcCfAc8frKu67t7kfSct/wDWGiLiQRz75eZLMnNp6H6qiYjfAf/PsZ9ozgYmq3eNgLMys+SMZ0gR\ncSlw0erN72TmF4fsp6rjnpsnKvvcNMQlqTDXxCWpMENckgozxCWpMENckgozxCWpsN8DMsDgQk1R\ntnQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb6823f9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.ix[5].plot(kind='bar'); plt.axhline(0, color='k') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['monospace'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAD0CAYAAABpeMZGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb6e226e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data frame\n",
    "df2 = pd.DataFrame(rand(10, 4), columns=['a', 'b', 'c', 'd'])\n",
    "df2.plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb67f6c2b0>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['monospace'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAD0CAYAAABpeMZGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nd4a5JBXAMJekAhjmklQAw1ySCvD/ADOSJIo8sap1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb681c9780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#stacked\n",
    "df2.plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb67e733c8>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['monospace'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWkAAAD3CAYAAADfYKXJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb67e02c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#stacked horizontal\n",
    "df2.plot(kind='barh',stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000-01-01         NaN\n",
      "2000-01-02    0.070913\n",
      "2000-01-03    0.133912\n",
      "2000-01-04    0.316185\n",
      "2000-01-05    1.616967\n",
      "2000-01-06    1.278182\n",
      "2000-01-07    1.790439\n",
      "2000-01-08    1.752253\n",
      "2000-01-09    2.207543\n",
      "2000-01-10    1.373701\n",
      "2000-01-11    0.852391\n",
      "2000-01-12    1.449482\n",
      "2000-01-13    1.778292\n",
      "2000-01-14    2.732603\n",
      "2000-01-15    1.696955\n",
      "2000-01-16    0.227658\n",
      "2000-01-17    1.026835\n",
      "2000-01-18    0.101492\n",
      "2000-01-19    1.344893\n",
      "2000-01-20    2.149443\n",
      "2000-01-21    2.392398\n",
      "2000-01-22    2.301847\n",
      "2000-01-23    0.934611\n",
      "2000-01-24    1.237767\n",
      "2000-01-25    1.083818\n",
      "2000-01-26    0.340141\n",
      "2000-01-27    0.338272\n",
      "2000-01-28    0.855412\n",
      "2000-01-29    1.337087\n",
      "2000-01-30    2.061731\n",
      "                ...   \n",
      "2002-08-28    8.155860\n",
      "2002-08-29    9.169072\n",
      "2002-08-30    7.986741\n",
      "2002-08-31    8.249927\n",
      "2002-09-01    7.884083\n",
      "2002-09-02    5.762010\n",
      "2002-09-03    5.211491\n",
      "2002-09-04    4.701458\n",
      "2002-09-05    4.406521\n",
      "2002-09-06    4.454511\n",
      "2002-09-07    4.272012\n",
      "2002-09-08    4.214813\n",
      "2002-09-09    5.948731\n",
      "2002-09-10    6.801059\n",
      "2002-09-11    5.427929\n",
      "2002-09-12    6.281387\n",
      "2002-09-13    6.927426\n",
      "2002-09-14    7.039186\n",
      "2002-09-15    6.609784\n",
      "2002-09-16    5.995434\n",
      "2002-09-17    7.575137\n",
      "2002-09-18    8.639217\n",
      "2002-09-19    7.871803\n",
      "2002-09-20    8.370408\n",
      "2002-09-21    7.162787\n",
      "2002-09-22    5.612626\n",
      "2002-09-23    6.427910\n",
      "2002-09-24    7.731969\n",
      "2002-09-25    7.645467\n",
      "2002-09-26    6.216898\n",
      "Freq: D, Name: A, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb67d764a8>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['monospace'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD3CAYAAAAJxX+sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb67d04ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram of a series. Note we apply diff to undo the cumsum before\n",
    "print (df['A'].diff())\n",
    "df['A'].diff().hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fcb67cc7fd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcb67bfe4a8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fcb67bc99b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcb67b8d1d0>]], dtype=object)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['monospace'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAEICAYAAACQzXX2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5C0ikiElbxGRDCl5i4hkSMlbRCRD/w9QS5FoEIkEuwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb67caa128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram of a Data Frame. Note we apply diff to undo the cumsum before\n",
    "df.diff().hist(color='k', alpha=0.5, bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Box Plots\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2         3         4\n",
      "0  0.426385  0.073490  0.656192  0.558762  0.238066\n",
      "1  0.831852  0.324104  0.246517  0.030236  0.633227\n",
      "2  0.919606  0.306596  0.225051  0.181491  0.330098\n",
      "3  0.909707  0.061220  0.445632  0.358637  0.209390\n",
      "4  0.003265  0.489948  0.563821  0.450856  0.511758\n",
      "5  0.989987  0.759497  0.087286  0.446989  0.209415\n",
      "6  0.234556  0.949227  0.817689  0.488496  0.368428\n",
      "7  0.827385  0.498589  0.705970  0.650829  0.392453\n",
      "8  0.193496  0.786077  0.407881  0.657887  0.635198\n",
      "9  0.560343  0.095492  0.529329  0.051978  0.336465\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['monospace'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAD3CAYAAADv7LToAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJBXAMJekAvwfyuYkuXPqBIsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb67b97c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(rand(10,5))\n",
    "print (df)\n",
    "bp = df.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['monospace'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEeCAYAAABmGcWlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfUkqiKEvSQUx9CWpIP8fAsDW/lL8PFgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb678951d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(rand(10,2), columns=['Col1', 'Col2'] )\n",
    "df['X'] = pd.Series(['A','A','A','A','A','B','B','B','B','B'])\n",
    "#group boxplot by a series\n",
    "bp = df.boxplot(by='X')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Area Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.19.2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb6787c470>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['monospace'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD3CAYAAAAJxX+sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb67792550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#note only available in pandas 0.14\n",
    "print (pd.__version__)\n",
    "df = pd.DataFrame(rand(10, 4), columns=['a', 'b', 'c', 'd'])\n",
    "df.plot(kind='area')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Scatter Matrix Plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fcb676bf278>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcb67b780f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcb676e0e80>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcb678c2e48>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fcb6768fb70>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcb6764e390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcb6761b668>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcb675de390>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fcb675aa860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcb6756e390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcb674ba860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcb674cba90>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fcb6744c860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcb67417c50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcb673dc780>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcb673a7b70>]], dtype=object)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['monospace'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAF/CAYAAABAAx5zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsBQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb67c70fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.tools.plotting import scatter_matrix\n",
    "df = pd.DataFrame(randn(1000, 4), columns=['a', 'b', 'c', 'd'])\n",
    "scatter_matrix(df, alpha=0.2, figsize=(6, 6), diagonal='kde')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Density Plots\n",
    "\n",
    "Plot an estimated probability density function (PDE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb60ca43c8>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['monospace'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAD3CAYAAAADtRO7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb60cac320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ser = pd.Series(numpy.random.randn(1000))\n",
    "ser.plot(kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Andrews Curves\n",
    "\n",
    "Andrews curves allow one to plot multivariate data as a large number of curves that are created using the attributes of samples as coefficients for Fourier series. By coloring these curves differently for each class it is possible to visualize data clustering. Curves belonging to samples of the same class will usually be closer together and form larger structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb60c78860>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['monospace'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAD3CAYAAAD8O/QcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWywWy0HAfwOc9LSZjbDIHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb603fe6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.tools.plotting import andrews_curves\n",
    "data = pd.read_csv('data/iris.dat')\n",
    "#print data\n",
    "andrews_curves(data, 'Name')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Parallel Coordinates\n",
    "\n",
    "Parallel coordinates is a plotting technique for plotting multivariate data. It allows one to see clusters in data and to estimate other statistics visually. Using parallel coordinates points are represented as connected line segments. Each vertical line represents one attribute. One set of connected line segments represents one data point. Points that tend to cluster will appear closer together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb601f79b0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['monospace'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAD3CAYAAAAg7kJuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb6040af60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.tools.plotting import parallel_coordinates\n",
    "parallel_coordinates(data, 'Name')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Rad Viz\n",
    "\n",
    "RadViz is a way of visualizing multi-variate data. It is based on a simple spring tension minimization algorithm. Basically you set up a bunch of points in a plane. In our case they are equally spaced on a unit circle. Each point represents a single attribute. You then pretend that each sample in the data set is attached to each of these points by a spring, the stiffness of which is proportional to the numerical value of that attribute (they are normalized to unit interval). The point in the plane, where our sample settles to (where the forces acting on our sample are at an equilibrium) is where a dot representing our sample will be drawn. Depending on which class that sample belongs it will be colored differently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb5af2f358>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['monospace'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAD3CAYAAAD8O/QcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb5af277f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.tools.plotting import radviz\n",
    "radviz(data, 'Name')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Exercise 6.1.c: Visual Analysis of Complaints in NYC\n",
    "\n",
    "Time to try it out by yourself. Go to to the exercise and try to solve it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# References\n",
    "\n",
    "* [Pandas Documentation](http://pandas.pydata.org/pandas-docs/stable/)\n",
    "* [Tutorials and Cookbooks] (http://pandas.pydata.org/pandas-docs/stable/tutorials.html)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700"
   },
   "moveMenuLeft": true,
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
